train_inputs, test_inputs, y_scaler = MIMO_fulldata_preparation(
df, n_test=4380, T=HORIZON, HORIZON=HORIZON, country=country)
concat_input = tf.concat([train_inputs['X'], train_inputs['X2']],
axis=2)
history = LSTMIMO.fit(concat_input,
train_inputs['target'],
batch_size=BATCHSIZE,
epochs=MAX_EPOCHS,
validation_split=0.15,
callbacks=[early_stopping,
WandbCallback()],
verbose=1)
concat_input = tf.concat([test_inputs['X'], test_inputs['X2']], axis=2)
FD_predictions = LSTMIMO.predict(concat_input)
FD_eval_df = create_evaluation_df(FD_predictions, test_inputs, HORIZON,
y_scaler)
preds, actuals = format_output(FD_eval_df)
preds = preds[np.where(preds.index.hour == 0)[0][0]:][::24]
actuals = actuals[np.where(actuals.index.hour == 0)[0][0]:][::24]
full = actuals.merge(preds,
how='inner',
left_index=True,
right_index=True,
suffixes=('_actuals', '_preds'))
full.to_csv('./results/' + dset + '/' + wandb.run.name + '_' + str(i) +
'.csv')
preds = flatten(preds.values.tolist())
actuals = flatten(actuals.values.tolist())
mae = validation(preds, actuals, 'MAE')
mape = validation(preds, actuals, 'MAPE')
rmse = validation(preds, actuals, 'RMSE')
history = LSTMIMO.fit(global_inputs_X, global_inputs_T, batch_size=BATCHSIZE, epochs=MAX_EPOCHS,
validation_split=0.15,
callbacks=[early_stopping, WandbCallback()], verbose=1)
val_acc_per_epoch = history.history['val_loss']
best_epoch = val_acc_per_epoch.index(min(val_acc_per_epoch)) + 1
print('Best epoch: %d' % (best_epoch,))
# Fit best model
final_model = LSTMIMO.fit(global_inputs_X,global_inputs_T,epochs=best_epoch,batch_size=BATCHSIZE,verbose=0)
metrics = pd.DataFrame(columns=['mae','mape', 'rmse', 'B'], index=range(28))
for i,df in enumerate(datasets):
concat_input = tf.concat([dX_test[i]['X'],dX_test[i]['X2']], axis=2)
FD_predictions = LSTMIMO.predict(concat_input)
FD_eval_df = create_evaluation_df(FD_predictions, dX_test[i], HORIZON, dX_scaler[i], dXtest_scaler[i])
preds, actuals = format_output(FD_eval_df)
preds = preds[np.where(preds.index.hour == 0)[0][0]:][::24]
actuals = actuals[np.where(actuals.index.hour == 0)[0][0]:][::24]
full = actuals.merge(preds, how='inner', left_index=True, right_index=True, suffixes=('_actuals', '_preds'))
full.to_csv('./results/'+dset+'/'+wandb.run.name+'_'+str(i)+'.csv')
preds = flatten(preds.values.tolist())
actuals = flatten(actuals.values.tolist())
mae = validation(preds, actuals, 'MAE')
mape = validation(preds, actuals, 'MAPE')
rmse = validation(preds, actuals, 'RMSE')
#print('rmse {}'.format(rmse))
metrics.loc[i] = pd.Series({'mae':mae, 'mape':mape, 'rmse':rmse, 'B': names[i]})
wandb.log({"mape": metrics.mape.mean()})
wandb.log({"rmse": metrics.rmse.mean()})
wandb.log({"mae": metrics.mae.mean()})
